Algorithms to Identify Preeclampsia in Routinely Collected Healthcare Data: A Systematic Review

Importance: Preeclampsia is a leading cause of maternal morbidity and mortality globally. As real-world data sources are increasingly used for maternal health surveillance and pharmacoepidemiologic studies, valid algorithms are essential for accurately identifying preeclampsia in routinely collected healthcare data.

Objective

To identify and characterize algorithms used to ascertain preeclampsia in population-based healthcare databases, summarize validation metrics, and describe the range of reported prevalence estimates. Evidence review: Following PRISMA guidelines of 2021, PubMed was searched for studies published from January 1, 2010, to July 1, 2025. Observational studies using reproducible code-based algorithms to identify preeclampsia in routinely collected healthcare data were included. Screening and full-text review were performed in duplicate, with discrepancies resolved by a third reviewer. Data extraction focused on algorithm definitions, coding systems, validation methods, and epidemiologic estimates. A total of 289 studies met inclusion criteria, including 13 validation studies.